Voici les résumés de leurs présentations :

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Palma Rozalia OSZTIAN

Computational thinking for all: a challenge of the XXI. century

Computational thinking (CT) and diagrammatic reasoning (DR) are important competencies from the perspectives of both Computer Science and engineering education. CT is often described as a critically important attitude or skill set for all students regardless of the educational program into which they are enrolled. Diagrammatic reasoning is commonly referred to as students' ability to think logically and solve complex problems. Accordingly, these two competencies are closely related and parallelly linked to several curriculum subjects during the educational process.

One way to improve students’ CT and DR could be the use of expressive online learning environments. The AlgoRythmics online platform that we created, is similar to other E-learning environments, but also unique in its way: as its name shows, it combines science with art (algorithm with rhythm, dance). Our main goal was to create an online learning environment, where the basic elements are the five learning steps: “the video, animation, in control and the code comes alive” learning steps.

Due to the combination of algorithms and dance, AlgoRythmics is a unique and artistically enhanced platform. Also, because it uses the dance choreographies, it is rich in human movement effect, which is also an interesting innovation in the field of teaching. Furthermore, the courses we create using this tool require the usage of different senses and also involve the user in the flow of education.

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Boroka OLTEAN-PETER

Comparing epidemiological models with the help of visualization dashboards

Leiras: In 2020, due to the COVID-19 pandemic, various epidemiological models appeared in major studies which differ in terms of complexity, type, etc. We are going to study that a complex model, which takes into consideration more parameters, is more accurate and gives more reliable results than a simpler one.

Our aim is to set up differential equation models, which rely on similar parameters, however, the systems of equation and number of parameters deviate from each other. A  visualization dashboard is implemented, and thus, we are able not only to study the models but also to make users understand the differences between the complexity of epidemiological models, and ultimately, to share a more specific overview about these that are defined by differential equations.

In order to validate our results, we make a comparison between the three models and the empirical data from Eastern Italy and Wuhan, based on the infectious cases of COVID-19. To validate our results, we calculate the values of the parameters using the Least Square optimization algorithm.